Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [76]
OK, what about baseball? Baseball fans pride themselves on their near-fanatical attention to every measurable detail of the game, from batting averages to pitching rotations. Indeed, an entire field of research called sabermetrics has developed specifically for the purpose of analyzing baseball statistics, even spawning its own journal, the Baseball Research Journal. One might think, therefore, that prediction markets, with their far greater capacity to factor in different sorts of information, would outperform simplistic statistical models by a much wider margin for baseball than they do for football. But that turns out not to be true either. We compared the predictions of the Las Vegas sports betting markets over nearly twenty thousand Major League baseball games played from 1999 to 2006 with a simple statistical model based again on home-team advantage and the recent win-loss records of the two teams. This time, the difference between the two was even smaller—in fact, the performance of the market and the model were indistinguishable. In spite of all the statistics and analysis, in other words, and in spite of the absence of meaningful salary caps in baseball and the resulting concentration of superstar players on teams like the New York Yankees and Boston Red Sox, the outcomes of baseball games are even closer to random events than football games.
Since then, we have either found or learned about the same kind of result for other kinds of events that prediction markets have been used to predict, from the opening weekend box office revenues for feature films to the outcomes of presidential elections. Unlike sports, these events occur without any of the rules or conditions that are designed to make sports competitive. There is also a lot of relevant information that prediction markets could conceivably exploit to boost their performance well beyond that of a simple model or a poll of relatively uninformed individuals. Yet when we compared the Hollywood Stock Exchange (HSX)—one of the most popular prediction markets, which has a reputation for accurate prediction—with a simple statistical model, the HSX did only slightly better.7 And in a separate study of the outcomes of five US presidential elections from 1988 to 2004, political scientists Robert Erikson and Christopher Wlezien found that a simple statistical correction of ordinary opinion polls outperformed even the vaunted Iowa Electronic Markets.8
TRUST NO ONE, ESPECIALLY YOURSELF
So what’s going on here? We are not really sure, but our suspicion is that the strikingly similar performance of different methods is an unexpected side effect of the prediction puzzle from the previous chapter. On the one hand, when it comes to complex systems—whether they involve sporting matches, elections, or movie audiences—there are strict limits to how accurately we can predict what will happen. But on the other hand, it seems that one can get pretty close to the limit of what is possible with relatively simple methods. By analogy, if you’re handed a weighted die, you might be able to figure out which sides will come up more frequently in a few dozen rolls, after which you would do well to bet on those outcomes. But beyond that, more elaborate methods like studying the die under a microscope to map out all the tiny fissures and irregularities on its surface, or building a complex computer simulation, aren’t going to help you much in improving your prediction.
In the same way, we found that with football games a single piece of information—that the home team wins slightly more than half the time—is enough to boost one’s performance in predicting the outcome above random guessing. In addition, a second simple insight, that the team with the better win-loss record should have a slight advantage, gives you another significant boost. Beyond that, however, all the additional information you might consider gathering—the